CIs: Progress bar, and start close to fail point using 1.96*SE?

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Hi,
Sitting watching R computing a pretty simple set of CIs for the last 4 hrs, and with no progress-bar feedback...

I wonder:
1. Why don't we use the cheap SEs from the 2nd derivative matrix to seed the starts for CI computation? What would be wrong with that?
2. Even with variance in how long it takes per CI, something like this would be helpful to the user:

Comments

1. There is no relationship between regular SEs and likelihood-based CIs. You can't see CI with SE.
2. You can get almost a progress bar by using checkpointing. Perhaps we should work on making checkpointing more user friendly to set up.

Are you suggesting using MLE + 2SE (or something like that) as a starting value for an upper confidence limit, and likewise MLE - 2SE for a lower confidence limit? With boundary conditions, that could lead to start values outside the feasible space. Plus, algebras don't have SEs to begin with.

I think this is generally a good idea. Whenever possible, start CI estimation near where it's probably going to be. The facts that 1. boundary conditions require adjustments, and 2. algebras don't have SEs, shouldn't stop us from making CIs work better in many use cases. When we can easily get good starting values for CIs, let's do it. When we can't, let's keep doing what we've been doing.

Thanks MikeH!
Made me wonder two things: Good starts for algebras, and Whether this is worth doing.

The doing part: I wonder how much of those 5 hours are spent getting close to the right value, and how much nudging it by tiny amounts to get the -2ll nudged by "exactly" 3.84? i.e., if we started with an 1.96*SE guess, how much would that save from the current process? (If the optimizer takes 3-steps to get as close our guess and then spends 6 more getting the precision, then this is a >30% speedup and worth having. But if we're getting into the ballpark in 3 steps then spending 30 more juggling everything else around the fixed parameter to target the desired -2lll drop off, then a big win would require a better optimiser or running on a 1000 core system and get their CI answers in a flat run time *.

On the algebra side, even though algebras don't have CIs, the user might know a lot about the CI.

For instance, I know in this case that they are just standardizations of some underlying matrices, and I bet thats by far the most common user-case for CI running on an algebra.

What if, like mxMultigroup(), we create a mxStandardization(A,B,C) function, which would allow OpenMx to imply the necessary information to translate the SE information on the input matrices into a start value for any particular algebra-cell output? (given that it now knows that Astd = A/(A+B+C) )?

* PS: What about a package (call it "R-torrent"), with sign-in as simple as logging into a website with your google id, and that then lets you use all the other R machines on the torrent to run your problems in exchange for running other people's when you have down time? I guess my R instance has something like 99.9% unused capacity, apart from these occasional death marches for CIs.

PPS: A progress bar using Rs built in progress API would be a lot nicer than checkpointing, although the implementation might be via a wrapper around auto-checkpointing when length(CIs)>1

PPPS: There are some nice progress system that generate notifications, like event notifications. So you could get a "weather report" type notification of "n/N CIs run" in your android or iOS notification pool.